Experimental On-Line Frequency Domain Identification and Adaptive Control of a Flexible Slewing Beam

1996 ◽  
Vol 118 (1) ◽  
pp. 58-65 ◽  
Author(s):  
R. I. Milford ◽  
S. F. Asokanthan

This paper presents experimental results for the real-time adaptive identification and control of a flexible, slewing beam. A frequency domain identification algorithm incorporating non-parametric transfer function estimation and least squares parametric estimation is used to reconstruct an accurate parametric model of the system, capable of accurately tracking changing plant dynamics in real time. This model is subsequently used to produce an LQG compensator which actively damps beam vibration caused by rapid slewing manoeuvres with large payload changes. Non-persistent excitation is addressed in the context of identification during nominal motion. It is shown that after a short duration learning period, the proposed identification scheme will yield a model which is sufficiently accurate for controller synthesis.

2011 ◽  
Vol 383-390 ◽  
pp. 4397-4404
Author(s):  
Zeng Liao ◽  
Cheng Peng ◽  
Yong Wang

The system identification problem of Multi-Input Multi-Output fractional order systems with Time-Delay is studied. A Frequency-Domain identification algorithm is presented, which combines genetic algorithm and subspace method for fractional order systems with time-delay in state. The genetic algorithm is used to identify fractional differential order and Time-Delay parameter. And the state space model is obtained by using frequency-domain subspace method when fractional differential order and time-delay parameter are fixed. Numerical simulation results validate the proposed algorithm.


1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.


Author(s):  
D.S. Bayard ◽  
F.Y. Hadaegh ◽  
Y. Yam ◽  
R.E. Scheid ◽  
E. Mettler ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document